Facing the incomplete information environment, the asynchronous neural virtual self-play (ANFSP) method allows AI to learn to generate optimal decisions in multiple virtual environments. The approach has performed well in Texas Hold’em and multiplayer FPS video games.
Baidu has released ERNIE (Enhanced Representation through kNowledge IntEgration), a new knowledge integration language representation model which outperforms Google’s state-of-the-art BERT (Bidirectional Encoder Representations from Transformers) in Chinese language tasks.
NVIDIA CEO and Co-Founder Jensen Huang says a rumored next-generation GPU architecture is not a priority for the company, and that he remains optimistic about clearing the chip inventory built up for cryptocurrency mining. Huang made the remarks in a press conference Tuesday at the GPU Technology Conference (GTC) in Santa Clara.
It is no secret that deep neural networks (DNNs) can achieve state-of-the-art performance in a wide range of complicated tasks. DNN models such as BigGAN, BERT, and GPT 2.0 have proved the high potential of deep learning. Deploying DNNs on mobile devices, consumer devices, drones and vehicles however remains a bottleneck for researchers.
DeepMind’s Research Platform Team has open-sourced TF-Replicator, a framework that enables researchers without previous experience with the distributed system to deploy their TensorFlow models on GPUs and Cloud TPUs. The move aims to strengthen AI research and development.
NVIDIA’s annual GPU Technology Conference (GTC) attracted some 9,000 developers, buyers and innovators to San Jose, California this week. CEO and Co-Founder Jensen Huang’s two-and-a-half hour keynote speech fused GPU-based innovations in domains ranging from graphic design to autonomous driving.
GTC 2019 runs next Monday through Thursday (March 18 — 21), and while we can only speculate what surprises NVIDIA CEO Jensen Huang might have in store for us, we can get some sense of where the company is headed by looking at what it’s been up to for the last 12 months.
Last October Stanford University announced plans to create an institute built for artificial intelligence research and development. Today, the school made good on its pledge, launching the Stanford Institute for Human-Centered Artificial Intelligence (Stanford HAI) with a mission “to advance AI research, education, policy, and practice to improve the human condition.”
Google yesterday announced a new program, Seasons of Docs, that aims to make a substantive contribution to open source software development. The eight-month project will assemble a team of technical writers to work on improving documentation development for various open source projects.
A new GitHub project, PyTorch Geometric (PyG), is attracting attention across the machine learning community. PyG is a geometric deep learning extension library for PyTorch dedicated to processing irregularly structured input data such as graphs, point clouds, and manifolds.
In a move that has surprised many, OpenAI today announced the creation of a new for-profit company to balance its huge expenditures into compute and AI talents. Sam Altman, the former president of Y Combinator who stepped down last week, has been named CEO of the new “capped-profit” company, OpenAI LP.
The University of California has halted all further subscriptions with one of the world’s largest scholarly publishers, Amsterdam-based Elsevier. The move follows more than six months of negotiations which failed to reach a substantial agreement on securing universal open access to UC research.
TensorFlow is the world’s most popular open source machine learning library. Since its initial release in 2015, the Google Brain product has been downloaded over 41 million times. At this week’s 2019 TensorFlow Dev Summit, Google announced a major upgrade on the framework, the TensorFlow 2.0 Alpha version.
Natural language processing has made significant progress in the past year, but few frameworks focus directly on NLP or sequence modeling. Google Brain recently released Lingvo, a deep learning framework based on TensorFlow. Synced invited Ni Lao, Chief Science Officer at Mosaix, to share his thoughts on Lingvo.
A paper recently accepted for ICLR 2019 challenges this with a novel optimizer — AdaBound — that authors say can train machine learning models “as fast as Adam and as good as SGD.” Basically, AdaBound is an Adam variant that employs dynamic bounds on learning rates to achieve a gradual and smooth transition to SGD.
The Conference on Computer Vision and Pattern Recognition (CVPR) is one of the world’s top computer vision (CV) conferences. CVPR 2019 runs June 15 through June 21 in Long Beach, California, and the list of accepted papers for the prestigious gathering has now been released.